Context Engineering Guide

Summary

A few years ago, many, even top AI researchers, claimed that prompt engineering would be dead by now. Obviously, they were very wrong, and in fact, prompt engineering is now even more important than ever. It is so important that it is now being rebranded as context engineering. Yes, another fancy term to describe the important process of tuning the instructions and relevant context that an LLM needs to perform its tasks effectively. Much has been written already about context engineering (Ankur Goyal, Walden Yan, Tobi Lutke, and Andrej Karpathy), but I wanted to write about my thoughts on the topic and show you a concrete step-by-step guide putting context engineering into action in developing an AI agent workflow.I am not entirely sure who coined context engineering, but we will build on this figure from Dex Horthy that briefly explains a bit about what context engineering is.I like the term context engineering as it feels like a broader term that better explains most of the work that goes into prompt engineering, including other related tasks.The doubt about prompt engineering being a serious skill is that many confuse it with blind prompting (a short task description you use in an LLM like ChatGPT). In blind prompting, you are just asking the system a question. In prompt engineering, you have to think more carefully about the context and structure of your prompt. Perhaps it should have been called context engineering from early on. Context engineering is the next phase, where you architect the full context, which in many cases requires going beyond simple prompting and into more rigorous methods to obtain, enhance, and optimize knowledge for the system.From a developer's point of view, context engineering involves an iterative process to optimize instructions and the context you provide an LLM to achieve a desired result. This includes having formal processes (e.g., eval pipelines) to measure whether your tactics are working. Given the fast evolution of the AI fi...